In [1]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
In [2]:
data = pd.read_csv('audi.csv')
data.head(5)
Out[2]:
model year price transmission mileage fuelType tax mpg engineSize
0 A1 2017 12500 Manual 15735 Petrol 150 55.4 1.4
1 A6 2016 16500 Automatic 36203 Diesel 20 64.2 2.0
2 A1 2016 11000 Manual 29946 Petrol 30 55.4 1.4
3 A4 2017 16800 Automatic 25952 Diesel 145 67.3 2.0
4 A3 2019 17300 Manual 1998 Petrol 145 49.6 1.0
In [3]:
import ydata_profiling as pf
display(pf.ProfileReport(data))

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In [4]:






# Removing duplicate columns
data = data.drop_duplicates()
data.reset_index(drop=True, inplace=True)





















In [5]:






display(pf.ProfileReport(data))

























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In [6]:






display(data.info)

























<bound method DataFrame.info of       model  year  price transmission  mileage fuelType  tax   mpg  engineSize
0        A1  2017  12500       Manual    15735   Petrol  150  55.4         1.4
1        A6  2016  16500    Automatic    36203   Diesel   20  64.2         2.0
2        A1  2016  11000       Manual    29946   Petrol   30  55.4         1.4
3        A4  2017  16800    Automatic    25952   Diesel  145  67.3         2.0
4        A3  2019  17300       Manual     1998   Petrol  145  49.6         1.0
...     ...   ...    ...          ...      ...      ...  ...   ...         ...
10560    A3  2020  16999       Manual     4018   Petrol  145  49.6         1.0
10561    A3  2020  16999       Manual     1978   Petrol  150  49.6         1.0
10562    A3  2020  17199       Manual      609   Petrol  150  49.6         1.0
10563    Q3  2017  19499    Automatic     8646   Petrol  150  47.9         1.4
10564    Q3  2016  15999       Manual    11855   Petrol  150  47.9         1.4

[10565 rows x 9 columns]>




















In [7]:






display(data.columns)

























Index(['model', 'year', 'price', 'transmission', 'mileage', 'fuelType', 'tax',
       'mpg', 'engineSize'],
      dtype='object')




















In [8]:






display (data.isna().sum())

























model           0
year            0
price           0
transmission    0
mileage         0
fuelType        0
tax             0
mpg             0
engineSize      0
dtype: int64




















In [9]:






display(data.dtypes)

























model            object
year              int64
price             int64
transmission     object
mileage           int64
fuelType         object
tax               int64
mpg             float64
engineSize      float64
dtype: object




















In [10]:






data['model'].unique()





















Out[10]:




array([' A1', ' A6', ' A4', ' A3', ' Q3', ' Q5', ' A5', ' S4', ' Q2',
       ' A7', ' TT', ' Q7', ' RS6', ' RS3', ' A8', ' Q8', ' RS4', ' RS5',
       ' R8', ' SQ5', ' S8', ' SQ7', ' S3', ' S5', ' A2', ' RS7'],
      dtype=object)




















In [11]:






data['transmission'].unique()





















Out[11]:




array(['Manual', 'Automatic', 'Semi-Auto'], dtype=object)




















In [12]:






data['fuelType'].unique()





















Out[12]:




array(['Petrol', 'Diesel', 'Hybrid'], dtype=object)




















In [13]:






X = data.iloc[:,[0,1,3,4,5,6,7,8]].values
display (X.shape)
display(pd.DataFrame(X).head(5))

























(10565, 8)




















0
1
2
3
4
5
6
7






0
A1
2017
Manual
15735
Petrol
150
55.4
1.4




1
A6
2016
Automatic
36203
Diesel
20
64.2
2.0




2
A1
2016
Manual
29946
Petrol
30
55.4
1.4




3
A4
2017
Automatic
25952
Diesel
145
67.3
2.0




4
A3
2019
Manual
1998
Petrol
145
49.6
1.0


























In [14]:






Y = data.iloc[:,[2]].values
display(Y.shape)
display(Y)

























(10565, 1)












array([[12500],
       [16500],
       [11000],
       ...,
       [17199],
       [19499],
       [15999]])




















In [15]:






from sklearn.preprocessing import LabelEncoder
le1 = LabelEncoder()
X[:,0] = le1.fit_transform(X[:,0])
le2 = LabelEncoder()
X[:,-4] = le2.fit_transform(X[:,-4])
display(pd.DataFrame(X))

































0
1
2
3
4
5
6
7






0
0
2017
Manual
15735
2
150
55.4
1.4




1
5
2016
Automatic
36203
0
20
64.2
2.0




2
0
2016
Manual
29946
2
30
55.4
1.4




3
3
2017
Automatic
25952
0
145
67.3
2.0




4
2
2019
Manual
1998
2
145
49.6
1.0




...
...
...
...
...
...
...
...
...




10560
2
2020
Manual
4018
2
145
49.6
1.0




10561
2
2020
Manual
1978
2
150
49.6
1.0




10562
2
2020
Manual
609
2
150
49.6
1.0




10563
9
2017
Automatic
8646
2
150
47.9
1.4




10564
9
2016
Manual
11855
2
150
47.9
1.4






10565 rows × 8 columns






















In [16]:






from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import ColumnTransformer
ct = ColumnTransformer(transformers = [('encoder',OneHotEncoder(),[2])],remainder='passthrough')
X = ct.fit_transform(X)
display (X.shape)
display (pd.DataFrame(X))

























(10565, 10)




















0
1
2
3
4
5
6
7
8
9






0
0.0
1.0
0.0
0
2017
15735
2
150
55.4
1.4




1
1.0
0.0
0.0
5
2016
36203
0
20
64.2
2.0




2
0.0
1.0
0.0
0
2016
29946
2
30
55.4
1.4




3
1.0
0.0
0.0
3
2017
25952
0
145
67.3
2.0




4
0.0
1.0
0.0
2
2019
1998
2
145
49.6
1.0




...
...
...
...
...
...
...
...
...
...
...




10560
0.0
1.0
0.0
2
2020
4018
2
145
49.6
1.0




10561
0.0
1.0
0.0
2
2020
1978
2
150
49.6
1.0




10562
0.0
1.0
0.0
2
2020
609
2
150
49.6
1.0




10563
1.0
0.0
0.0
9
2017
8646
2
150
47.9
1.4




10564
0.0
1.0
0.0
9
2016
11855
2
150
47.9
1.4






10565 rows × 10 columns






















In [17]:






display (pd.DataFrame(X))

































0
1
2
3
4
5
6
7
8
9






0
0.0
1.0
0.0
0
2017
15735
2
150
55.4
1.4




1
1.0
0.0
0.0
5
2016
36203
0
20
64.2
2.0




2
0.0
1.0
0.0
0
2016
29946
2
30
55.4
1.4




3
1.0
0.0
0.0
3
2017
25952
0
145
67.3
2.0




4
0.0
1.0
0.0
2
2019
1998
2
145
49.6
1.0




...
...
...
...
...
...
...
...
...
...
...




10560
0.0
1.0
0.0
2
2020
4018
2
145
49.6
1.0




10561
0.0
1.0
0.0
2
2020
1978
2
150
49.6
1.0




10562
0.0
1.0
0.0
2
2020
609
2
150
49.6
1.0




10563
1.0
0.0
0.0
9
2017
8646
2
150
47.9
1.4




10564
0.0
1.0
0.0
9
2016
11855
2
150
47.9
1.4






10565 rows × 10 columns






















In [18]:






from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X = sc.fit_transform(X)
display (pd.DataFrame(X))

































0
1
2
3
4
5
6
7
8
9






0
-0.582997
1.203038
-0.714096
-1.119276
-0.039002
-0.393254
1.053589
0.357402
0.351966
-0.884062




1
1.715274
-0.831229
-0.714096
-0.158819
-0.500425
0.479662
-0.951665
-1.571222
1.030836
0.111173




2
-0.582997
1.203038
-0.714096
-1.119276
-0.500425
0.212815
1.053589
-1.422867
0.351966
-0.884062




3
1.715274
-0.831229
-0.714096
-0.543002
-0.039002
0.042479
-0.951665
0.283224
1.269983
0.111173




4
-0.582997
1.203038
-0.714096
-0.735093
0.883845
-0.979108
1.053589
0.283224
-0.095471
-1.547551




...
...
...
...
...
...
...
...
...
...
...




10560
-0.582997
1.203038
-0.714096
-0.735093
1.345269
-0.892959
1.053589
0.283224
-0.095471
-1.547551




10561
-0.582997
1.203038
-0.714096
-0.735093
1.345269
-0.979961
1.053589
0.357402
-0.095471
-1.547551




10562
-0.582997
1.203038
-0.714096
-0.735093
1.345269
-1.038346
1.053589
0.357402
-0.095471
-1.547551




10563
1.715274
-0.831229
-0.714096
0.609547
-0.039002
-0.695585
1.053589
0.357402
-0.226616
-0.884062




10564
-0.582997
1.203038
-0.714096
0.609547
-0.500425
-0.558728
1.053589
0.357402
-0.226616
-0.884062






10565 rows × 10 columns


























MACHINE LEARNING¶






















Train-Test Split¶


















In [19]:






from sklearn.model_selection import train_test_split
(X_train,X_test,Y_train,Y_test) = train_test_split(X,Y,test_size=0.2,random_state=0)
print (X.shape, Y.shape)
print (X_train.shape, Y_train.shape)
print (X_test.shape, Y_test.shape)

























(10565, 10) (10565, 1)
(8452, 10) (8452, 1)
(2113, 10) (2113, 1)

























Random Forest Regressor¶


















In [20]:






from sklearn.ensemble import RandomForestRegressor
regression = RandomForestRegressor(random_state=0)
regression.fit(X_train,Y_train)

y_pred = regression.predict(X_test)

print(np.concatenate((y_pred.reshape(len(y_pred),1),Y_test.reshape(len(Y_test),1)),1))

























/Users/anirudhaswal/Library/jupyterlab-desktop/jlab_server/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)













[[34565.81 34991.  ]
 [16820.73 17299.  ]
 [11530.84 11444.  ]
 ...
 [18497.45 17670.  ]
 [17153.97 14290.  ]
 [18620.66 18990.  ]]





















In [21]:






from sklearn.metrics import r2_score,mean_absolute_error
print  ('R2 Score ', r2_score(Y_test, y_pred))
print  ('Mean Absolute Error', mean_absolute_error(Y_test,y_pred))

























R2 Score  0.9587306545197221
Mean Absolute Error 1517.4627789258122

























Linear Regression¶


















In [22]:






from sklearn.linear_model import LinearRegression
reg = LinearRegression()
reg.fit(X_train,Y_train)

y_pred = reg.predict(X_test)

print(np.concatenate((y_pred.reshape(len(y_pred),1),Y_test.reshape(len(Y_test),1)),1))

























[[31881.44873809 34991.        ]
 [19382.64884573 17299.        ]
 [13297.72442762 11444.        ]
 ...
 [18371.41128617 17670.        ]
 [20239.58731659 14290.        ]
 [17651.16128617 18990.        ]]





















In [23]:






print  ('R2 Score :', r2_score(Y_test, y_pred))
print  ('Mean Absolute Error :', mean_absolute_error(Y_test,y_pred))

























R2 Score : 0.7941884884766828
Mean Absolute Error : 3246.0428932841605





















In [24]:






print(len(data['price']))
print(len(y_pred))

























10565
2113





















In [25]:






y_pred = reg.predict(X)
display(y_pred)

LR_result = pd.concat([data['price'],pd.DataFrame(y_pred)],axis=1)
LR_result.rename(columns = {0 : 'prediction'}, inplace = True)
print(LR_result)

























array([[14859.66781983],
       [20417.94891489],
       [13616.79281983],
       ...,
       [19726.34869281],
       [21246.04998037],
       [16808.42498037]])












       price    prediction
0      12500  14859.667820
1      16500  20417.948915
2      11000  13616.792820
3      16800  20173.385955
4      17300  17646.098693
...      ...           ...
10560  16999  19489.598693
10561  16999  19595.223693
10562  17199  19726.348693
10563  19499  21246.049980
10564  15999  16808.424980

[10565 rows x 2 columns]

























Extra Tree Regressor¶


















In [26]:






from sklearn.ensemble import  ExtraTreesRegressor
ET_Model=ExtraTreesRegressor(n_estimators = 120)

ET_Model.fit(X_train,Y_train)
y_predict=ET_Model.predict(X_test)

print('R2 Score :', r2_score(Y_test, y_predict))
print('Mean Absolute Error :', mean_absolute_error(Y_test,y_predict))

























/Users/anirudhaswal/Library/jupyterlab-desktop/jlab_server/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)













R2 Score : 0.9618532738285344
Mean Absolute Error : 1527.9623872719148





















In [27]:






y_pred = reg.predict(X)
display(y_pred)
ET_result = pd.concat([data['price'],pd.DataFrame(y_pred)],axis=1)
ET_result.rename(columns = {0 : 'prediction'}, inplace = True)
display(ET_result)

























array([[14859.66781983],
       [20417.94891489],
       [13616.79281983],
       ...,
       [19726.34869281],
       [21246.04998037],
       [16808.42498037]])




















price
prediction






0
12500
14859.667820




1
16500
20417.948915




2
11000
13616.792820




3
16800
20173.385955




4
17300
17646.098693




...
...
...




10560
16999
19489.598693




10561
16999
19595.223693




10562
17199
19726.348693




10563
19499
21246.049980




10564
15999
16808.424980






10565 rows × 2 columns


























Cat Boost¶


















In [28]:






pip install catboost

























Requirement already satisfied: catboost in /Users/anirudhaswal/Library/jupyterlab-desktop/jlab_server/lib/python3.8/site-packages (1.2.5)
Requirement already satisfied: graphviz in /Users/anirudhaswal/Library/jupyterlab-desktop/jlab_server/lib/python3.8/site-packages (from catboost) (0.20.3)
Requirement already satisfied: matplotlib in /Users/anirudhaswal/Library/jupyterlab-desktop/jlab_server/lib/python3.8/site-packages (from catboost) (3.7.3)
Requirement already satisfied: numpy>=1.16.0 in /Users/anirudhaswal/Library/jupyterlab-desktop/jlab_server/lib/python3.8/site-packages (from catboost) (1.24.4)
Requirement already satisfied: pandas>=0.24 in /Users/anirudhaswal/Library/jupyterlab-desktop/jlab_server/lib/python3.8/site-packages (from catboost) (2.0.3)
Requirement already satisfied: scipy in /Users/anirudhaswal/Library/jupyterlab-desktop/jlab_server/lib/python3.8/site-packages (from catboost) (1.10.1)
Requirement already satisfied: plotly in /Users/anirudhaswal/Library/jupyterlab-desktop/jlab_server/lib/python3.8/site-packages (from catboost) (5.22.0)
Requirement already satisfied: six in /Users/anirudhaswal/Library/jupyterlab-desktop/jlab_server/lib/python3.8/site-packages (from catboost) (1.16.0)
Requirement already satisfied: python-dateutil>=2.8.2 in /Users/anirudhaswal/Library/jupyterlab-desktop/jlab_server/lib/python3.8/site-packages (from pandas>=0.24->catboost) (2.8.2)
Requirement already satisfied: pytz>=2020.1 in /Users/anirudhaswal/Library/jupyterlab-desktop/jlab_server/lib/python3.8/site-packages (from pandas>=0.24->catboost) (2023.3.post1)
Requirement already satisfied: tzdata>=2022.1 in /Users/anirudhaswal/Library/jupyterlab-desktop/jlab_server/lib/python3.8/site-packages (from pandas>=0.24->catboost) (2023.3)
Requirement already satisfied: contourpy>=1.0.1 in /Users/anirudhaswal/Library/jupyterlab-desktop/jlab_server/lib/python3.8/site-packages (from matplotlib->catboost) (1.1.1)
Requirement already satisfied: cycler>=0.10 in /Users/anirudhaswal/Library/jupyterlab-desktop/jlab_server/lib/python3.8/site-packages (from matplotlib->catboost) (0.12.1)
Requirement already satisfied: fonttools>=4.22.0 in /Users/anirudhaswal/Library/jupyterlab-desktop/jlab_server/lib/python3.8/site-packages (from matplotlib->catboost) (4.43.1)
Requirement already satisfied: kiwisolver>=1.0.1 in /Users/anirudhaswal/Library/jupyterlab-desktop/jlab_server/lib/python3.8/site-packages (from matplotlib->catboost) (1.4.5)
Requirement already satisfied: packaging>=20.0 in /Users/anirudhaswal/Library/jupyterlab-desktop/jlab_server/lib/python3.8/site-packages (from matplotlib->catboost) (23.2)
Requirement already satisfied: pillow>=6.2.0 in /Users/anirudhaswal/Library/jupyterlab-desktop/jlab_server/lib/python3.8/site-packages (from matplotlib->catboost) (10.1.0)
Requirement already satisfied: pyparsing>=2.3.1 in /Users/anirudhaswal/Library/jupyterlab-desktop/jlab_server/lib/python3.8/site-packages (from matplotlib->catboost) (3.1.1)
Requirement already satisfied: importlib-resources>=3.2.0 in /Users/anirudhaswal/Library/jupyterlab-desktop/jlab_server/lib/python3.8/site-packages (from matplotlib->catboost) (6.1.0)
Requirement already satisfied: tenacity>=6.2.0 in /Users/anirudhaswal/Library/jupyterlab-desktop/jlab_server/lib/python3.8/site-packages (from plotly->catboost) (8.2.3)
Requirement already satisfied: zipp>=3.1.0 in /Users/anirudhaswal/Library/jupyterlab-desktop/jlab_server/lib/python3.8/site-packages (from importlib-resources>=3.2.0->matplotlib->catboost) (3.17.0)
Note: you may need to restart the kernel to use updated packages.





















In [29]:






from catboost import CatBoostRegressor
cat = CatBoostRegressor()
cat.fit(X_train,Y_train)

























Learning rate set to 0.057364
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Out[29]:




<catboost.core.CatBoostRegressor at 0x151f55a00>




















In [30]:






cat_pred=cat.predict(X_test)
print('R2 Score :', r2_score(Y_test,cat_pred))

























R2 Score : 0.9621533622246573





















In [31]:






cat_pred=cat.predict(X)
CB_result = pd.concat([data['price'],pd.DataFrame(cat_pred)],axis=1)
CB_result.rename(columns = {0 : 'prediction'}, inplace = True)
display(CB_result)

































price
prediction






0
12500
14186.684563




1
16500
16588.373262




2
11000
12503.989081




3
16800
18740.156771




4
17300
18712.016277




...
...
...




10560
16999
19219.821014




10561
16999
18254.897647




10562
17199
18470.015620




10563
19499
20684.219975




10564
15999
17789.026696






10565 rows × 2 columns






















In [32]:






import pickle 
pickle.dump(cat, open('model.pkl','wb'))
model=pickle.load(open('model.pkl','rb'))
print (model.predict (X_train))

























[17654.41246426 32907.74112937 19885.15098691 ... 16410.67190012
 10594.84604682 26314.1863915 ]